min_forest: construct and visualize a minimal forest based on Chow-Liu...
In bAIo-lab/Questools: multivariate analysis and visualization

Description Usage Arguments Details Value Author(s) References Examples

Fits a minimal forest to data and visualizes it.

1 2	min_forest(data, stat = "BIC", community = TRUE, betweenness = TRUE, plot = FALSE, levels = NULL)

`data`	A normalized dataframe or matrix with no missing data of continuous and (or) categorical measurements.
`stat`	Measure to be minimized: LR, AIC, or BIC (the default). The default is BIC. It can also be a user-defined function with the format: FUN (model, dataset, previous, forbEdges); where the parameters are defined as in chStat. The function must return a structure as in chStat.
`community`	A logical value to show if the node communities should be detected and colored in the returned graph. (default = TRUE)
`betweenness`	A logical value to show if the node betweenness measurements should be computed and returned from the function. (default = TRUE)
`plot`	A logical value to show if the graph should be plotted. (default = FALSE)
`levels`	An integer value indicating the maximum number of levels of a categorical variable. To be used to distinguish the categorical variable. Defaults to NULL because it is supposed that `data` has been preprocessed using `data_preproc` and the categorical variables are specified. If it is set, first will run `data_preproc` to specify categorical and continuous variables.

The function is a wrapper for bnlearn package implementing several algorithms including Constraint-based algorithms (i.e., Max-Min Parents and Children, Semi-Interleaved HITON-PC, and Grow-Shrink), Score-based algorithms (i.e., Hill-Climbing and Tabu Search), and Hybrid algorithms (i.e., Max-Min Hill-Climbing), and Local Discovery algorithms (i.e, Max-Min Parents and Children and ARACNE). If one uses a more than one algorithm, the function combines all of the algorithms and returns a graph based on the combination. The graph is constructed based on the strength of associations calculated by bootstrapping.

a list containing:

`significanse`	A data.frame containing edges with p-statistics and p.values.
`summary`	a gRapHD object which is the fit model.
`graph`	an igraph object.
`betweenness`	betweenness measurements of each node.
`network`	a visNetwork plot of the graph.
`communities`	a named vector indicating the community of each node.

Elyas Heidari

Chow, C.K., and Liu, C.N. (1968) Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, Vol. IT-14, 3:462-7.

Edwards, D., de Abreu, G.C.G. and Labouriau, R. (2010). Selecting high- dimensional mixed graphical models using minimal AIC or BIC forests. BMC Bioinformatics, 11:18.

data("NHANES")
## Using raw data
mf <- min_forest(data = NHANES[1:1000, ], stat = "BIC", plot = TRUE, levels = 5)

## Using preprocessed data
data <- data_preproc(NHANES, levels = 15)
mf <- min_forest(data = data[1:1000, ], stat = "BIC", plot = FALSE)